Automated Cricket Commentary Generation for Videos

Cricket is one of the most popular sports in the world, and its fan base is growing rapidly. However, the quality of the commentary during cricket matches can vary widely, and it often relies on the subjective opinions of the commentators. In this paper, we present a groundbreaking approach to auto-generate cricket commentary using state-of-the-art computer vision and machine learning techniques. Our framework can automatically identify different events happening in a cricket video and produce insightful and engaging commentary based on the state of the game. We focus on the six essential cricket shots played by the batsman, including the Straight Drive, Cover Drive, Lofted shot, Sweep shot, and Cut Shot, and use a machine learning model to recognize each of these shots based on visual signals within the video. Along with that the length and line for the type of ball bowled has been analysed which facilitates a overall complete commentary. We also incorporate relevant data, such as the score, stage of the game, and players involved, to create contextually appropriate commentary. Our approach has the potential to revolutionize the field of sports commentary, providing a new and immersive experience for cricket fans worldwide. With our innovative approach, we believe that auto-generated commentary can become an integral part of the live sports viewing experience, enhancing fan engagement and providing new opportunities for sports broadcasters and content creators.